Sleep Duration and Amyloid β Among Cognitively Healthy Later-Life Adults: A Systematic Review and Meta-Analysis

Background Amyloid β (Aβ) is a hallmark of Alzheimer’s disease (AD). Insufficient sleep duration and poor sleep quality have been found to be a risk factor of developing AD because sleep may involve regulating Aβ. However, the magnitude of the relationship between sleep duration and Aβ is still unclear. This systematic review examines the relationship between sleep duration and Aβ in later-life adults. Methods We screened 5,005 published articles searched from relevant electronic databases (i.e., PubMed, CINAHL, Embase, and PsycINFO) and reviewed 14 articles for the qualitative synthesis and 7 articles for the quantitative synthesis. Results Mean ages of the samples ranged from 63 to 76. Studies measured Aβ using cerebrospinal fluid, serum, and positron emission tomography scans with two tracers: Carbone 11-labeled Pittsburgh compound B or fluorine 18–labeled. Sleep duration was subjectively measured using interviews, questionnaires, or using objective measures such as polysomnography or actigraphy. The studies accounted for demographic and lifestyle factors in their analyses. Five of the 14 studies reported a statistically significant association between sleep duration and Aβ. Using seven eligible articles, our quantitative synthesis demonstrated that the average association between sleep duration and Aβ was not statistically significant (Fisher’s Z = −0.006, 95% CI= −0.065 ~ 0.054). Conclusion This review suggests that caution should be taken when considering sleep duration as the primary factor for Aβ levels. More studies are needed using a longitudinal design, comprehensive sleep metrics, and larger sample sizes to advance our understanding of the optimal sleep duration and AD prevention.

searching articles for systematic reviews. The ow diagram in Fig. 1 provides details on the search strategy and the number of articles each database yielded. Comprehensive strategies, including both index and keyword methods, were devised by the librarian and primary author for the following databases: PubMed, CINAHL (EBSCO platform), Embase (Elsevier platform), and PsycINFO (EBSCO platform). To maximize sensitivity, no pre-established database lters other than the English language lter were used. The full PubMed search strategy, as detailed in the supplemental table A, was also adapted for the other databases. In addition to the database searches, references and cited papers of the 1,156 relevant papers were located using the Scopus database.
Inclusion criteria for the qualitative synthesis were as follows: 1) observational studies with a longitudinal or cross-sectional design, 2) includes exposure variables of sleep duration, 3) has Amyloid β plaques (e.g., Aβ, Aβ 42 , Aβ 40 , Aβ 42 /Aβ 40 ) as the outcome, 4) a human study of adults aged ≥ 50 years old, and 5) recruited (or included) cognitively healthy individuals. An additional inclusion criterion for the quantitative synthesis was studies that reported su cient data for examining the effect sizes, such as Pearson's correlation (r), means, standard deviations, t, F, or X 2 values. We excluded studies 1) not written in English, 2) interventional studies, 3) non-peer reviewed papers, proceedings, editorials, and reviews, and 4) the study sample focused only on neurological conditions or sleep disorders. For the quantitative synthesis, we excluded studies that lacked or had inadequate inferential statistical results for calculating the effect size.
The initial search yielded 6,987 articles. After removing 1,982 duplicate articles, a total of 5,005 articles were imported to the web-based systematic review application, Rayyan. Four authors (CM, KV, HD, MZ) screened the abstracts and titles of the 5,005 articles based on the eligibility criteria. Then, additional articles were removed leaving 61 full-text articles that were reviewed by four authors (CM, AS, YC, MZ). A total of 14 articles met the inclusion criteria for the qualitative synthesis and studies met the criteria for quantitative synthesis (Fig. 1). Disagreements were solved through discussion among all authors until consensus was reached.

Quality analysis
The risk bias of the selected papers was assessed independently by two reviewers using the National Institute of Health Study Quality Assessment Tool (2019) for Observational Cohort and Cross-Sectional Studies (https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools). The internal validity of the studies were assessed based on 14 domains: 1) bias due to an unclear purpose, 2) bias due to an unclear speci cation of the population, 3) bias due to ineligible participants, 4) bias due to recruitment from a different population, 5) bias due to unclear power justi cation, 6) bias due to measure timing, 7) bias due to time frame, 8) bias due to outcome level, 9) bias due to invalid exposure measure, 10) bias due to frequency of the assessments, 11) bias due to the outcome measure, 12) bias due to an unblinded assessment, 13) bias due to loss during follow up, and 14) bias due to statistical analysis and confounding.
For each domain, we categorized the risk of bias as either low or high risk. We rated an item "unclear risk" if there was no information about the risk of bias.

Statistical analysis
We aggregated the effect sizes across the studies and calculated the publication bias, overall effect sizes, and Q statistics using Comprehensive Meta-Analysis (CMA) version 4 software (Biostat, Inc). We also calculated the effect sizes using Pearson's r as the effect size index after examining the available information on the correlation between sleep duration and Aβ. The r was extracted from each study and converted to Fisher's Z = 0.5*Log(1 + Corr)/(1-Corr) [31]. We used a Q statistic to evaluate the heterogeneity of variance. We also calculated the I 2 index using I 2 = 100% × (Q − degree of freedom)/Q to identify how the variance in observed effects re ected the variance in true effects rather than by random error. The random-effects model was applied for the current study because we expect that the sampling distribution varied across the studies and parameters were drawn from random variables [32][33][34][35][36]. To consider the possibility of sampling bias from all possible samples, we assessed the studies for publication bias. First, we visually inspected the studies for symmetry of the funnel plot (supplemental gure A). Second, we ran the Tweedie's Trim and Fill test to ensure that the publication bias could not reverse our estimate of the effect sizes. [31] Results Thirteen articles were included in the qualitative synthesis portion of this review and six studies were included for the quantitative synthesis. Table 1 summarizes study information on study sample, design, and assessments. Table 2 summarizes study results and relevant information. Standardized interview of mean number of hours of sleep obtained each night during the prior month using the following response options: "more than 7"; "more than 6, up to 7"; "more than S, up to 6"; or "5 or fewer."   Continuous ("more than 7"; "more than 6, up to 7"; "more than 5, up to 6"; or "5 or fewer" were coded in 0 to 5) Qualitative Synthesis
The mean age of the samples ranged from 61.6 to 75.7. Data from the studies included study subjects, and most studies had speci c inclusion criteria for the mean age and cognitive status [37][38][39][40][41][42][43][44][45][46][47][48][49][50]. All studies only included individuals who were cognitively healthy without any neurological or untreated psychological conditions or certain health conditions that may affect sleep and Aβ. Exclusion criteria for all studies in our analysis were low cognition or markers associated with cognitive impairment such as lesions, stroke, or neurological disorders [37-44,  Quality Assessment Figure 2 illustrates the assessment of the risk of bias categories. Among the 13 observational studies, three had a moderate to high risk of bias due to measurement timing, the outcome measure, exposure measure, or the population and participants. Two of the articles had a risk of bias related to a small sample size and population without any power justi cation. Two of the studies reported a high risk of bias related to the measurement timing, timeframe, and outcome and exposure variable. Eight of the articles had a risk of bias related to the exposure measure using a self-report sleep question or questionnaire. Five articles had a risk of bias due to the limited number of confounding variables.

Sleep Measures
Both subjective and objective sleep measures were used in the reviewed studies (Table 1)
Association Between Exposure And Outcomes Table 2 describes the ndings of the reviewed studies for the qualitative synthesis. Five of the 14 articles [39,[47][48][49][50] found that shorter sleep duration was associated with higher Aβ. However, three of the studies reported the reverse association between sleep duration and PET-measured global and regional Aβ

Quantitative synthesis
Six eligible articles were used for the quantitative synthesis because the remaining studies were not included due to a lack of reporting Pearson's correlation or not having a mean sleep duration for the amyloid beta categories. Figure 3 and Table 3 Table 3, for heterogeneity, our results indicate a Q-value of 3.101 with 6 degrees of freedom, indicating that the amount of between-study variance in the observed effect is less than we expected based on sampling error alone. The I 2 statistic is 0%, indicating that 0% of the variance in observed effects re ect the variance in true effects rather than sampling error. Tau re ects the standard deviation of true effect size, which is 0.000 in Fisher's Z units. As shown in the standard error funnel plot by Fisher's Z (supplemental Figure A), the plot is slightly asymmetric, indicating that there could be minor publication bias from the included studies. This might be due to either our inability to identify studies with non-signi cant nings or failing to report nonsigni cant ndings [31]. We ran the Tweedie's Trim and Fill method, which demonstrated that even if we remove one study, the effect size remains statistically insigni cant (Fisher's Z= -0.0056, 95% CI= -0.0649 ~ 0.0537). This nding may imply that some of the articles might not have presented the ndings due to non-signi cant results.

Discussion
Our review synthesized fourteen studies for the qualitative synthesis and seven studies for the quantitative synthesis focusing on sleep duration and Aβ. Using seven eligible articles, our quantitative synthesis demonstrated that the average association between sleep duration and Aβ was not statistically signi cant (Fisher's Z = -0.013, 95% CI= -0.084 ~ 0.058). This review suggests that caution should be taken when considering sleep duration as the primary factor for Aβ levels. The studies used subjective questionnaires or questions, PSG, or actigraphy to measure sleep duration. Aβ was measured using PET CT, CSF, or serum.
There are quality concerns about a few studies due to the small sample size, timing of the measures, limited use of comprehensive measurements, and confounding variables.
Although not all 14 qualitative studies we reviewed demonstrated statistically signi cant associations between sleep duration and Aβ, ve studies showed that shorter sleep duration was associated with greater Aβ, but one study reported sleep great than 8 hours is associated with Aβ burden. The quantitative synthesis revealed an effect size of -0.006, but it was not statistically signi cant, indicating that sleep duration may not be a primary factor in Aβ accumulation. Alternatively, these results may be due to moderators (i.e., APOE4, sex, age, family history, or unmeasured moderators), heterogenous outcome types of Aβ that may not provide consistent ideas, or publication bias due to insigni cant results not being favorable for publication. Prior research has demonstrated that chronic sleep restriction or deprivation of slow wave sleep can alter the diurnal uctuation of CSF Aβ levels [24,25,51,52]. Sleep deprivation may also impair human memory consolidation, in part by reducing the synthesis of proteins needed to support synaptic plasticity [14,[53][54][55]. In a meta-analysis by Wu and colleagues (2018), the authors suggest that there is a U-shaped relationship between sleep duration and cognitive disorders. Compared to the reference group (7-8 hours per day), individuals with a short or long duration had a higher risk of developing cognitive disorders, such as Alzheimer's disease or dementia [56]. Both a shorter sleep duration (< 7 hours / night) and poor subjective sleep quality are important for cognitive function [57] or brain structures and functions [58,59]. However, more studies in this eld need using larger sample sizes, a prospective design, and publication of magnitude of correlations of null results can shed light on the true relationship between sleep duration and Aβ.
There was considerable heterogeneity in the methods used in the reviewed studies to conclude the relationship between sleep duration and Aβ burden. In addition to the considerable variability in sleep measurements, Aβ was also measured in different ways: PET to quantify the Aβ burden, CSF, and serum sample. Studies using PET used different tracers (e.g., 11C-PiB, 18F-orbetapir, 18F-utemetamol) as well as different quanti cation methods. Most studies focused on global Aβ burden in brain, but assessing both the overall levels of Aβ in PET as well as speci c regional deposition could help us understand areas of the brain that may be affected more than other areas. This variability across the measurements prevented us from conducting a meta-analysis and drawing strong conclusions. However, it is promising in the current eld of science to review data across different measures of Aβ accumulation. Although AD can be diagnosed at an autopsy [64], the US National Institute on Aging and Alzheimer's Association have suggested using Aβ as well as tau and neurodegeneration to de ne and diagnose AD in both symptomatic and a symptomatic stages [65]. Increased accessibility to biomarkers and the potential for blood biomarkers or additional biomarkers in addition to Aβ would provide further information about the underlying disease processes in the future.  [41], sleep quality [39], frequent napping [37], longer sleep latency [42,44], greater sleep fragmentation [44], a higher apnea hypopnea index, and slow wave sleep time [38,40]. These results may indicate that different dimensions of sleep could contribute more to Aβ burden than quantity of sleep.
The reviewed studies accounted for various demographic and clinical confounders in the multivariate models. Most of the reviewed studies accounted for age and sex, which are well-known confounders [10,[66][67][68]. Individuals with sleep disorders, underlying health or psychological conditions, medications, genetic factors, social determinants, high fat diets, and physical activity have different sleep quantity and quality [69][70][71]. These factors may also increase the amount of Aβ accumulation [72][73][74][75][76][77][78][79]. These confounding factors may play critical roles in determining the association between sleep duration and Aβ burden.
However, the studies used a cross-sectional design, which prevents us from drawing causal relationships. Speci cally, some of the studies did not measure sleep and Aβ burden in a similar time period, and thus the results may not re ect a direct link between the two factors. Although researchers have speculated that the relationship between sleep and AD pathology could be bidirectional, there is limited evidence to support the longitudinal relationships [21,80].
The strength of this review is that we examined current evidence related to sleep duration and amyloid burden. However, there are a few limitations. First, the study did not test for moderating effects of age, sex, APOE4 status, or sleep e ciency. Second, the current study only included publications written in English even though some important ndings may have been published in different languages. Third, this review focused only on sleep duration even though other speci c sleep characteristics could have more in uence on Aβ pathology.

Conclusions
The results of this systematic review suggest that previous studies that have demonstrated an association between sleep duration and Aβ accumulation should be understood cautiously. Researchers would greatly bene t from more studies using a longitudinal design, comprehensive sleep measure, broad range of biomarkers, and larger sample sizes to advance our scholarly understanding of the relationship between sleep and AD.

vii) Acknowledgment
We would like to thank Jennifer Deberg, a medical librarian (blinded for review), at the University of Iowa Hardin Library for Health Sciences for assisting with the development of the search strategy for all web databases used for this study. Also, we thank Dr. Joel Geerling for advice and input on the qualities of the studies and clinical implications. Risk of bias summary Figure 3 Forest plot of overall Fisher's Z score for association between sleep duration with Amyloids beta.

Supplementary Files
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